首先，傳統的關聯法則，使用者必須不斷的試誤（包含：屬性的挑選、門檻值的設定等…法則產生前的相關程序與步驟），俾便找出具解釋能力的關聯法則。再者，與近期相關研究相比，資料採礦資料都是以資料是精確且乾淨為前提的，在這樣的條件下所產生的關聯法則，可能會發生在某些特定情況下（例如：有人為輸入的錯誤、記錄錯誤等…不完整資料），符合條件的規則被淘汰亦或產生過多的規則。最後，透過相關研究的文獻探討，發現約略集理論已成功的被運用在選擇屬性及改變效率之決策問題上。因此，本研究選擇以約略集理論為研究的理論基礎，從縮短決策者探勘關聯法則的試誤時間為解決問題的方向，在規則產生前，利用集合的產生，針對資料型態涉及順序尺度或含區間資料的順序尺度，提供新的演算概念。希冀，在不失去原本的排序關係的前提下，提供更多的排序資訊予決策者使用。
研究中，針對順序尺度與含區間資料的順序尺度，分別提出約略關聯法則的探勘步驟、演算流程說明、應用於酒精飲料產品與非酒精飲料的案例，以及提供相關個案的管理意涵。最後，將本研究所未考量到的部分以及可以持續研究的方向分段論述，讓後續的相關研究學者可以參考。

First, as per the traditional association rules, in order to identify meaningful association rules, the user must use trial and error method （including attribute choice, threshold value hypothesis, etc., considering the procedure and step taken before the association rules were formulated）. Furthermore, unlike algorithm-related research, data mining algorithms assumed that input data were accurate; however, the assumption would not be made in case one best rule exists for each particular situation such as input mistake or record mistake and similar incomplete data. Finally, through literature review, rough set theory has been successfully applied in deriving decision trees/rules and specifying problems, with proven effectiveness in selecting attributes. Therefore, we select rough set theory on the basis of our research, and this reduces the time that policymakers take to determine meaningful association rules. Before the rule is formulated, through the set process, we provide a new algorithm for the data type that involves ordinal data and ordinal data with internal data. Under a condition that does not affect the sorting relations between the values of the ordinal data, we provide more sorting information that the policymakers can use.
In the research, we provide two new algorithms that are suitable for ordinal data and ordinal data with internal data. Further, we provide illustrative examples using alcoholic and non-alcoholic beverage products individually. Finally, we give some suggestions for future research.

目次
謝辭	I
中文摘要	II
英文摘要	III
目次	V
表目錄	VIII
圖目錄	X
第一章 緒論	1
1.1研究背景與動機	1
1.2研究問題與目的	2
1.3研究方法與流程	3
第二章 文獻探討	4
2.1約略集理論	4
2.1.1	一般的約略集（A general view of rough sets）	5
2.1.2	變精度約略集（variable precision rough set/VPRS）	6
2.1.3	約略集理論的好處及應用的領域	7
2.1.4	約略集與各領域的結合	10
2.1.5	約略集理論與本研究的關係	11
2.2關聯法則	12
2.2.1	關聯法則的定義	12
2.2.2	從分群或分類討論關聯法則	13
2.2.3	從產生規則集討論關聯法則	16
2.2.4	從資料維度討論關聯法則	17
2.2.5	關聯法則的改良	18
2.2.6	關聯法則與本研究的關係	19
2.3相關研究綜合討論	20
2.3.1	資料採礦與約略集理論	20
2.3.2	相關研究使用的資料型態與衡量尺度	21
2.3.3	以約略集理論為基礎的相關研究	23
2.3.4	約略集理論與模糊理論及之比較	23
第三章 探勘順序尺度約略關聯法則	25
3.1研究問題	25
3.2順序尺度的約略關聯法則探勘步驟	26
3.3順序尺度的約略關聯法則演算流程	33
3.4順序尺度的約略關聯法則應用在非酒精飲料	36
3.5順序尺度的約略關聯法則應用在非酒精飲料管理意涵	42
3.5.1	從法則產生效率與效能的角度與傳統關聯法則比較	42
3.5.2	從法則資訊提供的角度與過去的研究比較	44
3.5.3	順序尺度的約略關聯法則在行銷策略上的運用	46
第四章 探勘含區間資料的順序尺度約略關聯法則	48
4.1研究問題	48
4.2含區間資料的順序尺度約略關聯法則探勘步驟	49
4.3含區間資料的順序尺度約略關聯法則演算流程	57
4.4含區間資料的順序尺度約略關聯法則應用在酒精飲料	61
4.5含區間資料的順序尺度約略關聯法則應用在酒精飲料管理意涵	69
4.5.1	與傳統關聯法則比較	69
4.5.2	含區間資料的順序尺度約略關聯法則建立產業的競爭力	70
4.5.3	含區間資料的順序尺度約略關聯法則在行銷策略上的應用	70
第五章 結論與後續研究	72
5.1研究結論	73
5.2後續研究	74
5.2.1	從相對的概念討論順序尺度	74
5.2.2	發展階層概念的順序尺度約略關聯規則	75
5.2.3	發展決策支援系統	76
5.2.4	從關聯規則門檻值設定改善	77
5.2.5	發展推薦機制探勘改變行為	77
參考文獻	78
附錄-問卷	87
 
表目錄
表2-1約略集理論的好處	7
表2-2約略集理論運用的領域	9
表2-3約略集理論與各理論結合之應用	10
表2-4從分類觀點討論或改良關聯法則	13
表2-5從分群觀點討論或改良關聯法則	14
表2-6從產生規則集討論關聯法則	16
表2-7從資料維度討論關聯法則	18
表2-8關聯法則的改良	18
表2-9相關研究使用的資料型態與衡量尺度	22
表3-1習慣飲用「非酒精類飲料」的排序資料表	25
表3-2資訊系統表	27
表3-3順序性資料的核心屬性值	28
表3-4決策資料表	29
表3-5不可辨識關係下的屬性值	31
表3-6非酒精飲料資訊表	36
表3-7基本統計表	38
表3-8非酒精飲料偏好排序核心屬性集合	39
表3-9非酒精飲料偏好的約略關聯法則集合	40
表3-10非酒精飲料偏好的傳統關聯法則集合	41
表3-11APRIORI產生的關聯法則	43
表3-12非酒精飲料偏好的消費者行為規則集合	47
表4-1資訊系統表	50
表4-2啤酒「品牌回想」的排序資料表	50
表4-3「年齡及收入」與品牌回想間的關係	53
表4-4以台灣啤酒為主的決策資料表	54
表4-5不可辨識關係下的資料集合	56
表4-6酒精飲料資訊表	61
表4-7基本統計表	63
表4-8屬值屬性與決策屬性間的潛在關係	64
表4-9酒精飲料的品牌回想排序總值	65
表4-10酒精飲料偏好的約略關聯法則集合	66
表4-11酒精飲料偏好的傳統關聯法則集合	68
表4-12傳統關聯法則產生的酒精飲料偏好的規則集合	69
表4-13酒精飲料偏好的消費者行為規則集合	70
 
圖目錄
圖1-1 研究流程圖	3
圖2-1 文獻探討架構圖	4
圖3-1約略集理論的上界與下界概念	32
圖3-2 資料節點串流圖	44
圖3-3分群後的非酒精飲料產品光譜圖	44
圖3-4探勘核心屬性後的非酒精飲料產品光譜圖	45
圖4-1品牌權益的概念模式	48
圖4-2考量品牌回想排序總值的酒精飲料品牌光譜圖	65
圖5-1順序尺度資料的階層約略關聯展開樹	75

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